We further apply contextual dropout to the attention layers of VQA models, whose goal is to provide an answer to a question relevant to the content of a given image. We conduct experiments on the commonly used benchmark, VQA-v2 [1]}, containing human-annotated question-answer (QA) pairs.
There are three types of questions: Yes/No, Number, and Other. In Figure REF , we show one example for each question type. There are 10 answers provided by 10 different human annotators for each question (see explanation of evaluation metrics in Appendix REF ). As shown in the examples, VQA is generally so challenging that there are often several different human annotations for a given image. Therefore, good uncertainty estimation becomes even more necessary.
